Practical Methodology and Ethics in AI

Practical Methodology and Ethics in AI

This course is part of Foundations of Neural Networks Specialization

Instructor: Zerotti Woods

Sponsored by Louisiana Workforce Commission

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

3 weeks at 2 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

6 hours to complete

3 weeks at 2 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Learners will gain hands-on experience training and debugging deep learning models while considering deployment challenges and best practices.
Students will understand and evaluate ethical concerns in AI, including bias, fairness, and the societal impact of deploying neural networks.
Learners will explore how to integrate structured probabilistic models with deep learning, reducing uncertainty and improving model decision-making.

Details to know

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Assessments

6 assignments

Taught in English

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This course is part of the Foundations of Neural Networks Specialization

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There are 4 modules in this course

The course "Practical Methodologies and Ethics in AI" equips learners with the essential skills needed to build, evaluate, and deploy deep learning models, while also addressing critical ethical considerations in AI. Through hands-on projects and case studies, you’ll explore the practical methodologies used to train models effectively, troubleshoot issues, and apply structured probabilistic approaches to manage uncertainty. A key highlight of the course is its emphasis on ethics, enabling you to identify and address bias, fairness, and societal implications throughout the AI lifecycle. By integrating structured probabilistic models with deep learning, you’ll gain the ability to create robust, interpretable AI systems that tackle real-world challenges.

"Practical Methodology and Ethics in AI" focuses on teaching essential skills in dataset exploration, training deep learning models, and deploying them, with a strong emphasis on ethics in the AI lifecycle. The course covers identifying and addressing bias and fairness issues and integrating probabilistic models with deep learning to manage uncertainty. This course provides a solid foundation in both technical and ethical aspects for responsible AI development.

What's included

2 readings

This module will discuss practical methodologies for training Deep Learning Models. Students will explore case studies along with different situations to apply previous and new knowledge in the process of training and deploying Deep Learning Models.

What's included

1 video1 reading2 assignments

This module will discuss ethical considerations for Deep Learning Models. You will explore nuances of ethics and the use of machine learning to make decisions.

What's included

1 video1 reading2 assignments

This lesson delves into the intersection of structured probabilistic models and deep neural networks, highlighting how probabilistic frameworks can be integrated with Deep Learning to model uncertainty, learn from incomplete data, and provide interpretable AI systems.